Jing Dai

24 papers receiving 314 citations

Peers

Jing Dai
Comparison fields: 5 of 70
  • Industrial and Manufacturing Engineering 64
  • Safety, Risk, Reliability and Quality 42
  • Medical Laboratory Technology 6
  • Signal Processing 37
  • Control and Systems Engineering 60
Replace Junchen Guo with:
Junchen Guo China
Peng Dai China
Xiaoxi Hu China
Gabriel Michau Switzerland
Chenyang Li China
Jiang Liu China
Gavneet Singh Chadha Germany
Jing Dai relative to Junchen Guo China Junchen Guo's profile →
Citations per field
00.5×1.5×2.4×
Junchen Guo · 1×
Citations per year

Countries citing papers authored by Jing Dai

Since Specialization
Citations

This map shows the geographic impact of Jing Dai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jing Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jing Dai more than expected).

Fields of papers citing papers by Jing Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jing Dai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jing Dai. The network helps show where Jing Dai may publish in the future.

Co-authors

The 25 scholars most cited alongside Jing Dai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jing Dai Line = papers co-authored together Jing Dai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020114
2 202145
3 201837
4 201624
5 202419
6 200318
7 201810
8 20199
9 20238
10 20208
11 20164
12 20204
13
On-line Fault Diagnose of Distribution System Based on Modified Rough Sets Reduction Algorithm
20073
14
Rapid Eye Localization Based on Projection Peak
20093
15 20173
16
Behavior Analysis Based SMS Spammer Detection in Mobile Communication Networks
20162
17
Novel Approach for Aviation Electromechanical System Testability Modeling and Analysis
20102
18 20202
19 20241
20 20141

About Jing Dai

Jing Dai is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Information Systems and Computer Networks and Communications, having authored 25 papers that have together received 321 indexed citations. Recurring topics across this work include Fault Detection and Control Systems (4 papers), Anomaly Detection Techniques and Applications (4 papers), Spam and Phishing Detection (2 papers), Traffic and Road Safety (2 papers), Video Surveillance and Tracking Methods (2 papers), Advanced Computational Techniques and Applications (2 papers), Remote Sensing and LiDAR Applications (2 papers) and Engineering and Test Systems (2 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (64 citations), Safety, Risk, Reliability and Quality (42 citations), Medical Laboratory Technology (6 citations), Signal Processing (37 citations) and Control and Systems Engineering (60 citations). Jing Dai has collaborated with scholars based in China, Italy and Singapore. Frequent co-authors include Diyin Tang, Jinsong Yu, Yue Song, Mong Li Lee, Wynne Hsu, Yunan Li, Yining Quan, Xiaoyi Feng, Zhaoqiang Xia and Pengfei Xu. Their work appears in journals such as IEEE Transactions on Instrumentation and Measurement, Neurocomputing, International Journal of Machine Learning and Cybernetics, International Journal of Environmental Research and Public Health and Journal of Intelligent & Fuzzy Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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